Quality Of Measured Data Research Articles

Whole building validation for simulation programs including synthetic users and heating systems: experimental design

A large-scale study for validating building energy simulation programs against measured data was undertaken within IEA EBC Annex 71 “Building energy performance assessment based on optimized in-situ measurements” as a more complex and realistic successor of the dataset created previously in IEA EBC Annex 58. The validation method consists of a set of high quality measurement data and a precise documentation of all boundary conditions. This enables a user to create a complete model of the different validation scenarios. The results of this model can be compared to the real measurement data. Because of the detailed modelling, the remaining deviations should indicate the limitations of the tool under investigation. The definition of the scenarios consists of extensive weather data and a detailed description of the building geometry, components compositions, thermal bridges, air tightness, ventilation, etc. In addition to the previous Annex 58 dataset this experiment contains synthetic users with internal heat and moisture gains, operated doors and windows and underfloor heating with an air source heat pump. This paper sets out the experimental design, a key element in ensuring a useful experimental dataset.

Optimising drone flight planning for measuring horticultural tree crop structure

In recent times, multi-spectral drone imagery has proved to be a useful tool for measuring tree crop canopy structure. In this context, establishing the most appropriate flight planning variable settings is an essential consideration due to their controls on the quality of the imagery and derived maps of tree and crop biophysical properties. During flight planning, variables including flight altitude, image overlap, flying direction, flying speed and solar elevation, require careful consideration in order to produce the most suitable drone imagery. Previous studies have assessed the influence of individual variables on image quality, but the interaction of multiple variables has yet to be examined. This study assesses the influence of several flight variables on measures of data quality in each processing step, i.e. photo alignment, point cloud densification, 3D model building, and ortho-mosaicking. The analysis produced a drone flight planning and image processing workflow that delivers accurate measurements of tree crops, including the tie point quality, densified point cloud density, and the measurement accuracy of height and plant projective cover derived from individual trees within a commercial avocado orchard. Results showed that flying along the hedgerow, at high solar elevation and with low image pitch angles improved the data quality. Optimal flying speed needs to be set to achieve the required forward overlap. The impacts of each image acquisition variable are discussed in detail and protocols for flight planning optimisation for three scenarios with different drone settings are suggested. Establishing protocols that deliver optimal image acquisitions for the collection of drone data over horticultural tree crops, will create greater confidence in the accuracy of subsequent algorithms and resultant maps of biophysical properties.

Indicators of Quality of Clinical Care for Type 2 Diabetes Patients in Primary Health Care Centers in Qatar: A Retrospective Analysis.

Background Despite the high prevalence of type 2 diabetes mellitus in Gulf countries, standards of diabetes care at the primary care level have not been widely studied. Aim To compare the results of diabetes clinical indicators from the American Diabetes Association (ADA) 2017 guidelines to the reference benchmarks in the Behavioral Risk Factor Surveillance System. Materials and Methods A cross-sectional analysis of electronic medical records in 643 randomly selected adult patients with type 2 diabetes was undertaken. A checklist enabled the collection of sociodemographic, clinical, biochemical, and quality measurement data. Data were analyzed using Stata 9.0. The chi-squared test was used to compare two or more proportions. Results There were 643 patients (male = 60.3%; female = 39.7%), and the majority (71.7%) aged between 40 and 64 years. Common comorbidities were dyslipidemia (72.3%), hypertension (70%), obesity (50.1%), and preobesity (overweight) (37.9%). Over 15% were smokers. The most commonly prescribed diabetes medications were metformin (89.9%), dipeptidyl peptidase-4 inhibitors (61.1%), and sulfonylureas (49.3%). Only 35.5% (p < 0.0001) of patients met the reference glycated hemoglobin (HbA1c) cutoff level of 7.0%. The reference level for blood pressure control was met by 70.2% (p < 0.0001) and for low-density lipoprotein cholesterol, 73.8% (p < 0.0001). Albuminuria was present in 39.2%, and very low vitamin D level (<20 ng/ml) in 39.1%. Most patients had annual foot (89.6%, p < 0.0001) and eye (72.3%, p < 0.0001) examinations. Only 39.9% had referrals for dietary counseling, and there were lower rates of referrals and uptake for pneumococcal, influenza, and hepatitis B vaccines. Most (76.2%) did not have screening for depression. Conclusion The majority of the results met the ADA standards, while glycemic control, dietary counseling, and screening for depression were poor in comparison to the standards. Continuing education for clinicians, patient education for self-management, and targeted weight management are recommended.

Faulty-data detection and data quality measure in cyber–physical systems through Weibull distribution

The preeminent challenge when data is compiled from cyber–physical system is the detection and filtering of faulty data. When the storage, processing, and communication of a reasonable amount of data migrate to those mobile and embedded devices, it becomes strenuous to apply conventional powerful algorithms for identifying, eliminating, and tolerating the faulty data because of the resource constraints of these devices. Further, the real-time requirements of CPS applications require CPSs to respond quickly and the aggregation method that is long-established in CPSs makes it challenging to ascertain the value of the original data. Thus, a systematic approach is essential to accomplish the goal of faulty data detection and filtering. In this paper, we present several approaches that we can deploy to detect and filter faulty data efficiently and cost-effectively, to improve the quality of the collected data from a system’s perspective to match the specific requirements of CPSs.

Robust extended Kalman filter based state estimation for nonlinear dynamic processes with measurements corrupted by gross errors

Abstract The quality of on-line measurement data usually cannot meet the demands of practical process monitoring and control due to the influence of measurement noise. Although extended Kalman filter (EKF) is able to improve the quality by reconciling the measurement data to fit the dynamic process model describing nonlinear and Gaussian processes, it rarely considers the presence of different types of gross errors, such as outliers, bias and drift. However, the three types of gross errors often simultaneously appear in dynamic process systems. We propose a robust EKF combined with measurement compensation (MC-REKF) approach, in which a statistical test method is used to detect the abnormal measurement data, where gross error identification is accomplished within a moving window. Subsequently, the magnitudes of gross errors are estimated and used for measurement compensation. Finally, the compensated measurements are updated to re-estimate the accurate states via EKF. The effectiveness of the proposed MC-REKF is demonstrated through a complex nonlinear dynamic chemical process system, namely the free radical polymerization of styrene. With three different types of gross errors, the mean squared error (MSE) of reconciled measurements based on the MC-REKF decreases 37 fold compared to EKF. The magnitude of the residuals between the estimated states and the true states falls below 1.0E−6 when using the MC-REKF. The implementation results imply that the proposed MC-REKF can identify and estimate different types of gross errors and finally decreases their influence on state estimation and measurement reconciliation.

Implementing a Novel Quality Improvement-Based Approach to Data Quality Monitoring and Enhancement in a Multipurpose Clinical Registry.

Objective:To implement a quality improvement based system to measure and improve data quality in an observational clinical registry to support a Learning Healthcare System.Data Source:ImproveCareNow Network registry, which as of September 2019 contained data from 314,250 visits of 43,305 pediatric Inflammatory Bowel Disease (IBD) patients at 109 participating care centers.Study Design:The impact of data quality improvement support to care centers was evaluated using statistical process control methodology. Data quality measures were defined, performance feedback of those measures using statistical process control charts was implemented, and reports that identified data items not following data quality checks were developed to enable centers to monitor and improve the quality of their data.Principal Findings:There was a pattern of improvement across measures of data quality. The proportion of visits with complete critical data increased from 72 percent to 82 percent. The percent of registered patients improved from 59 percent to 83 percent. Of three additional measures of data consistency and timeliness, one improved performance from 42 percent to 63 percent. Performance declined on one measure due to changes in network documentation practices and maturation. There was variation among care centers in data quality.Conclusions:A quality improvement based approach to data quality monitoring and improvement is feasible and effective.

Eye tracking in developmental cognitive neuroscience – The good, the bad and the ugly

Eye tracking is a popular research tool in developmental cognitive neuroscience for studying the development of perceptual and cognitive processes. However, eye tracking in the context of development is also challenging. In this paper, we ask how knowledge on eye-tracking data quality can be used to improve eye-tracking recordings and analyses in longitudinal research so that valid conclusions about child development may be drawn. We answer this question by adopting the data-quality perspective and surveying the eye-tracking setup, training protocols, and data analysis of the YOUth study (investigating neurocognitive development of 6000 children). We first show how our eye-tracking setup has been optimized for recording high-quality eye-tracking data. Second, we show that eye-tracking data quality can be operator-dependent even after a thorough training protocol. Finally, we report distributions of eye-tracking data quality measures for four age groups (5 months, 10 months, 3 years, and 9 years), based on 1531 recordings. We end with advice for (prospective) developmental eye-tracking researchers and generalizations to other methodologies.

Signal Processing in fNIRS: A Case for the Removal of Systemic Activity for Single Trial Data.

Researchers using functional near infrared spectroscopy (fNIRS) are increasingly aware of the problem that conventional filtering methods do not eliminate systemic noise at frequencies overlapping with the task frequency. This is a problem when signals are averaged for analysis, even more so when single trial data are used as in online neurofeedback or BCI applications where insufficiently preprocessed data means feeding back noise instead of brain activity or when looking for brain-behavior relationships on a trial-by-trial basis. For removing this task-related noise statistical approaches have been proposed. Yet as evidence is lacking on how these approaches perform on independent data, choosing one approach over another can be difficult. Here signal quality at the single trial level was considered together with statistical effects to inform this choice. Compared were conventional band-pass filtering and wavelet minimum description length detrending and the combination of both with a more elaborate, published preprocessing approach for a motor execution—motor imagery data set. Temporal consistency between Δ[HbO] and Δ[HbR] and two measures of the spatial specificity of signals that are proposed here served as measures of data quality. Both improved strongly for the combinationed preprocessing approaches. Statistical effects showed a strong tendency toward getting smaller for the combined approaches. This underlines the importance to adequately deal with noise in fNIRS recordings and demonstrates how the quality of statistical correction approaches can be estimated.

Measuring data quality in information systems research

Although contemporary research relies to a large extent on data, data quality in Information Systems research is a subject that has not received much attention until now. In this paper, a framework is presented for the measurement of scientific data quality using the principles of rule-based measurement. The proposed framework is capable of handling data quality problems due to both incorrect execution and incorrect description of data collection and validation processes. It is then argued that uncertainty can arise during the measurement, which complicates data quality assessment. The framework is therefore extended to handle uncertainty about the truth value of predicates. Instead of a numerical quality level, data quality is then expressed as either a probability distribution or a possibility distribution over the ordinal quality scale. Finally, it is also shown how quality thresholds can be formulated based on the results of the quality measurement. The usefulness of the proposed framework is illustrated throughout the paper with an example of the construction of a possible survey data quality measurement system and, subsequently, the application of that system on a realistic example.

Data quality and the Internet of Things

The Internet of Things (IoT) is driving technological change and the development of new products and services that rely heavily on the quality of the data collected by IoT devices. There is a large body of research on data quality management and improvement in IoT, however, to date a systematic review of data quality measurement in IoT is not available. This paper presents a systematic literature review (SLR) about data quality in IoT from the emergence of the term IoT in 1999 to 2018. We reviewed and analyzed 45 empirical studies to identify research themes on data quality in IoT. Based on this analysis we have established the links between data quality dimensions, manifestations of data quality problems, and methods utilized to measure data quality. The findings of this SLR suggest new research areas for further investigation and identify implications for practitioners in defining and measuring data quality in IoT.

Assessment of the quality of accession describing metadata on plant genetic resources

In the wide-ranging field of biodiversity conservation, genebanks play a major role in the preservation of cultivated plants. An important focus of genebanks is the comprehensive documentation of the maintained material. This is a prerequisite to enable users to select the most suitable material for e.g. research or breeding programs (Hoisington et al. 1999). The German Federal ex situ Genebank for Agricultural and Horticultural Crops, which is being hosted at IPK, is the largest genebank in Western Europe. Within the multitude of data associated with plant material (e.g. from various -omics areas or conservation management), the so-called passport data represent the most original and oldest data in genebanks. These metadata are often subject to heterogeneity due to historically different collection and curation, especially if they were received from different institutions around the world. This leads to difficulties in handling these data and can result in misinterpretations. In addition, there are correlations between the individual attributes of the passport data which can lead to a different importance of the individual data points for the users. Major challenges for users are to estimate completeness, correctness and reliability of these data. Thus, it is necessary to assess the quality of these data by defining a suitable set of metrics. Unfortunately, classical data quality measurement metrics, e.g. (Klier 2008), are not sufficient to fulfill the users' needs. Depending on the intention of the user, a different focus is placed on the data. Moreover, the individual attributes of the respective areas can be related to each other. Therefore, a single index value for estimating the quality of a passport record is not sufficient. Rather, it seems to be more promising to generate more differentiated quality statements. We are working on a metrics system that is sensitive to the users' focus. Through a practical set of rules of data quality metrics for accession-related data, the user will be able to influence the weighting of individual domains (e.g. geographical origin, biological status) according to their context (fit-for-use index). The presentation will discuss the background and will give an overview of the progress of this research activity.

Using iNaturalist in a Coverboard Protocol to Measure Data Quality: Suggestions for Project Design

We evaluated the accuracy of data records generated by citizen scientist participants using iNaturalist in a coverboard sampling scheme, a common method to detect herpetofauna, involving 17 species of amphibians and reptiles. We trained and observed 10 volunteers working over an eight-month period at a study site in Sonoma County, California, USA. A total number of 1,169 observations were successfully uploaded to the iNaturalist database by volunteers including a new locality for Black Salamander. Volunteers were generally successful in collecting and uploading data for verification but had more difficulties detecting smaller, camouflaged salamanders and photographing quick-moving lizards. Errors associated with uploading smartphone data were reduced with volunteer experience. We evaluated all observations and determined that 82% could be verified to the species level based on the photograph. Forty-five percent of the observations made it to the iNaturalist “research grade” quality using their crowdsourcing tools. Independently we evaluated the crowdsourcing accuracy of the research grade observations and found them to be 100% accurate to the species level. A variety of factors (herpetofauna group, species, photograph quality) played a role in determining whether observations were elevated to research grade. Volunteer screening and training protocols emphasizing smartphones that have appropriate battery and data storage capacity eliminated some issues. Our results suggest that a variety of factors can help scientists and resource managers improve the experiences and quality of the data in citizen science biodiversity programs.

Performance Measurement and the Kidney Quality Improvement Registry.

In the 2015 Medicare Access and CHIP Reauthorization Act, the Centers for Medicare and Medicaid Services (CMS) began the transition to a value-based payment system through implementation of the Quality Payment Program (QPP) ([1][1]). Representing a paradigm shift away from traditional volume-based

Do Distractions During Web Survey Completion Affect Data Quality? Findings From a Laboratory Experiment

Web survey respondents are frequently distracted during survey completion, which potentially affects the quality of data they provide. This article reports on results from a laboratory experiment examining how distractions during web survey completion influence data quality. Participants were randomly assigned to experimental groups using a 2 (device type) × 3 (form of distraction) between-subject factorial design. They were asked to complete a web questionnaire on either a PC or a tablet and were allocated to one of the three distraction conditions: (1) the presence of other people in the room who have a loud conversation, (2) the presence of music, or (3) no distraction. The study examines the effect of distraction on various measures of data quality and attentiveness. While participants felt significantly more distracted in the presence of other people or music, the study found no significant effect of distraction for any of the data quality and attentiveness measures. The findings are encouraging for survey practitioners: Even if web respondents listen to music or are in noisy environments, these forms of distraction generally do not seem to affect the quality of responses they provide.

Measuring Trust in Medical Researchers: Adding Insights from Cognitive Interviews to Examine Agree-Disagree and Construct-Specific Survey Questions.

While scales measuring subjective constructs historically rely on agree-disagree (AD) questions, recent research demonstrates that construct-specific (CS) questions clarify underlying response dimensions that AD questions leave implicit and CS questions often yield higher measures of data quality. Given acknowledged issues with AD questions and certain established advantages of CS items, the evidence for the superiority of CS questions is more mixed than one might expect. We build on previous investigations by using cognitive interviewing to deepen understanding of AD and CS response processing and potential sources of measurement error. We randomized 64 participants to receive an AD or CS version of a scale measuring trust in medical researchers. We examine several indicators of data quality and cognitive response processing including: reliability, concurrent validity, recency, response latencies, and indicators of response processing difficulties (e.g., uncodable answers). Overall, results indicate reliability is higher for the AD scale, neither scale is more valid, and the CS scale is more susceptible to recency effects for certain questions. Results for response latencies and behavioral indicators provide evidence that the CS questions promote deeper processing. Qualitative analysis reveals five sources of difficulties with response processing that shed light on under-examined reasons why AD and CS questions can produce different results, with CS not always yielding higher measures of data quality than AD.

An Assessment of Overdose Surveillance at a Local Public Health Department

ObjectiveTo assess the data sources used to monitor overdose events in Marion County and improve community overdose surveillance.IntroductionMirroring public health response to infectious disease outbreaks, many public health departments are taking an outbreak management approach to respond to drug overdose surges 1-3. The Marion County Public Health Department (MCPHD) has developed an overdose response plan (ORP) integrating drug overdose surveillance and community stakeholder response strategies. Effective drug overdose surveillance requires accurate and reliable data streams. This work assessed data sources utilized for county overdose surveillance and provided recommendations to improve overdose surveillance.MethodsData sources utilized as of September 2018 for opioid overdose surveillance in Marion County were assessed on utilization history by epidemiologists. General recommendations to improve overdose surveillance were created based on the findings. The three primary sources were emergency department data, ambulance run data, and death certificate data. Secondary sources included Indiana Poison Center (IPC) and toxicology data. General recommendations were generated based on challenges/solutions encountered and good practices observed from other health departments 4,5.ResultsThe assessment of data sources and utilization showed variation of data entry at the hospital level, limited identifiers in some cases, and varying timeliness ranges which may limit combined use of many data sources. The emergency department data source showed particular variation in data entry, limited unique identifier information), and no incident location information which impedes geographical surveillance. Periodic data checks by the ambulance service data holder appears to drastically increase data quality. Intermittent data feed drops from specific emergency departments also interfered with effective surveillance. Recommendations were generated based on lessons learned during successful partnerships with Indianapolis Emergency Medical Services, IPC, and emergency departments and challenges encountered during overdose surveillance work (Figure 1).In application of the strategy, the MCPHD is interested in linking data and looking for other ways to improve our overdose response to get a fuller picture of what is happening with overdoses, so we applied the steps in figure one to find areas of improvement. We found that limited identifiers and incomplete fields are our biggest challenge to linking datasets, so to combat these gaps we identified sources that have the necessary fields of interest and have been working with others to improve the data quality. Additionally, data sources will be evaluated on experiences with three categories: completeness in data fields, timeliness of data delivery, and consistency of data feed. Data quality measures were developed for completeness by fields present per record, timeliness by lag time from time added to time of event, and consistency by record counts per facility over time. We also recognized that meeting with partners is necessary to share how we are using the data and additional datasets that we might use in the future. Additionally we have been meeting with academic researchers so that we can expand our analyses to identify other issues related to overdoses. Finally, in order to make a difference in Marion County we are applying our findings to our outreach and interventions to hopefully prevent more overdoses and deaths.Future plans include data partnerships include police drug arrest data, fire department naloxone administration data, prescription drug monitoring data, Medicaid claims data, and health information exchange overdose data. Future research partnerships will consider a solutions based framework 6.ConclusionsThe results of our work demonstrate the value in surveillance assessment to summarize limitations of the many data sources utilized at a local level to conduct overdose surveillance. Our evaluation approach provides a path to improve and fill in surveillance gaps with new processes. Other health departments interested in optimizing overdose surveillance may seek a similar evaluation approach. Periodic data linkages have not been implemented which presents an opportunity to glean valuable insights on longitudinal patterns of drug use in the population. Future collaboration with researchers presents an opportunity to improve MCPHD ORP, Safe Syringe Access and Support Program, and Substance Use Outreach Services interventions.

Unmanned Aerial System Imagery, Land Data and User Needs: A Socio-Technical Assessment in Rwanda

Unmanned Aerial Systems (UAS) are emerging as a tool for alternative land tenure data acquisition. Even though UAS appear to represent a promising technology, it remains unclear to what extent they match the needs of communities and governments in the land sector. This paper responds to this question by undertaking a socio-technical study in Rwanda, aiming to determine the match between stakeholders’ needs and the characteristics of the UAS data acquisition workflow and its final products as valuable spatial data for land administration and spatial planning. A needs assessment enabled the expression of a range of land information needs across multiple levels and stakeholder sectors. Next to the social study, three different UAS were flown to test not only the quality of data but the possibilities of the use of this technology within the current institutional environment. A priority list of needs for cadastral and non-cadastral information as well as insights into operational challenges and data quality measures of UAS-based data products are presented. It can be concluded that UAS can have a significant contribution to match most of the prioritized needs in Rwanda. However, the results also reveal that structural and capacity conditions currently undermine this potential.

Real-time Feedback in Pay-for-Performance: Does More Information Lead to Improvement?

Pay-for-performance (P4P) has been used expansively to improve quality of care delivered by physicians. However, to what extent P4P works through the provision of information versus financial incentives is poorly understood. To determine whether an increase in information feedback without changes to financial incentives resulted in improved physician performance within an existing P4P program. Implementation of a new registry enabling real-time feedback to physicians on quality measure performance. Observational, predictive piecewise model at the physician-measure level to examine whether registry introduction associated with performance changes. We used detailed physician quality measure data 3years prior to registry implementation (2010-2012) and 2years after implementation (2014-2015). We also linked physician-level data including age, gender, and board certification; group-level data including registry click rates; and patient panel data including chronic conditions. Four hundred thirty-four physicians continuously affiliated with Advocate from 2010 to 2015. Physician performance on ten quality metrics. We found no consistent pattern of improvement associated with the availability of real-time information across ten measures. Relative to predicted performance without the registry, average performance increased for two measures (childhood immunization status-rotavirus (p < 0.001) and diabetes care-medical attention for nephropathy (p = 0.024)) and decreased for three measures (childhood immunization status-influenza (p < 0.001) and diabetes care-HbA1c testing (p < 0.001) and poor HbA1c control (p < 0.001)). Results were consistent for subgroup analysis on those most able to improve, i.e., physicians in the bottom tertile of performance prior to registry introduction. Physicians who improved most were in groups that accessed the registry more than those who improved least (8.0 vs 10.0 times per week, p = 0.010). More frequent provision of information, provided in real-time, was insufficient to improve physician performance in an existing P4P program with high baseline performance. Results suggest that electronic registries may not themselves drive performance improvement. Future work should consider testing information feedback enhancements with financial incentives.

Axon Registry® data validation: Accuracy assessment of data extraction and measure specification.

To conduct a data validation study encompassing an accuracy assessment of the data extraction process for the Axon Registry®. Data elements were abstracted from electronic health records (EHRs) by an external auditor (IQVIA) using virtual site visits at participating sites. IQVIA independently calculated Axon Registry quality measure performance rates based on American Academy of Neurology measure specifications and logic using Axon Registry data. Agreement between Axon Registry and IQVIA data elements and measure performance rates was calculated. Discordance was investigated to elucidate underlying systemic or idiosyncratic reasons for disagreement. Nine sites (n = 720 patients; n = 80 patients per site) with diversity among EHR vendor, practice settings, size, locations, and data transfer method were included. There was variable concordance between the data elements in the Axon Registry and those abstracted independently by IQVIA; high match rates (≥92%) were observed for discrete elements (e.g., demographics); lower match rates (<44%) were observed for elements with free text (e.g., plan of care). Across all measures, there was a 76% patient-level measure performance agreement between Axon Registry and IQVIA (κ = 0.53, p < 0.001). There was a range of concordance between data elements and quality measures in the Axon Registry and those independently abstracted and calculated by an independent vendor. Validation of data and processes is important for the Axon Registry as a clinical quality data registry that utilizes automated data extraction methods from the EHR. Implementation of remediation strategies to improve data accuracy will support the ability of the Axon Registry to perform accurate quality reporting.

Medical data quality assessment: On the development of an automated framework for medical data curation

Data quality assessment has gained attention in the recent years since more and more companies and medical centers are highlighting the importance of an automated framework to effectively manage the quality of their big data. Data cleaning, also known as data curation, lies in the heart of the data quality assessment and is a key aspect prior to the development of any data analytics services. In this work, we present the objectives, functionalities and methodological advances of an automated framework for data curation from a medical perspective. The steps towards the development of a system for data quality assessment are first described along with multidisciplinary data quality measures. A three-layer architecture which realizes these steps is then presented. Emphasis is given on the detection and tracking of inconsistencies, missing values, outliers, and similarities, as well as, on data standardization to finally enable data harmonization. A case study is conducted in order to demonstrate the applicability and reliability of the proposed framework on two well-established cohorts with clinical data related to the primary Sjögren's Syndrome (pSS). Our results confirm the validity of the proposed framework towards the automated and fast identification of outliers, inconsistencies, and highly-correlated and duplicated terms, as well as, the successful matching of more than 85% of the pSS-related medical terms in both cohorts, yielding more accurate, relevant, and consistent clinical data.